In this Data Mining Fundamentals tutorial, we discuss the curse of dimensionality and the purpose of dimensionality reduction for data preprocessing. When dimensionality increases, data becomes increasingly sparse in the space that it occupies. Dimensionality reduction will help you avoid this.
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Most of the datasets you'll find will have more than 3 dimensions. How are you supposed to understand visualize n-dimensional data? Enter dimensionality reduction techniques. We'll go over the the math behind the most popular such technique called Principal Component Analysis.
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I used a screengrab from 3blue1brown's awesome videos: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw
More learning resources:
https://plot.ly/ipython-notebooks/principal-component-analysis/
https://www.youtube.com/watch?v=lrHboFMio7g
https://www.dezyre.com/data-science-in-python-tutorial/principal-component-analysis-tutorial
https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/
http://setosa.io/ev/principal-component-analysis/
http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html
https://algobeans.com/2016/06/15/principal-component-analysis-tutorial/
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Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is.
In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained.
There is a minor error at 1:47: Points 5 and 6 are not in the right location
If you are interested in doing PCA in R see: https://youtu.be/0Jp4gsfOLMs
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Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/
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Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in

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Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
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#ScikitLearn #DimentionalityReduction #PCA #SVD #MachineLearning #DataAnalytics #DataScience
Dimensionality reduction is an important step in data pre processing and data visualisation specially when we have large number of highly correlated features.
In this tutorial, we apply Principal Component Analysis and Singular Value decomposition to boston housing and MNIST handwriting dataset and observe the effects of dimensionality reduction on accuracy.
We also see how dimensionality reduction can be used to visualize data.
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This is a ~3-minute video highlight produced by undergraduate students Robert Colgan and David Gutierrez regarding their research topic during the 2013 AMALTHEA REU Program at Florida Institute of Technology in Melbourne, FL. They were mentored by MS student Jugesh Sundram and professor Dr. G. Bhaskar Tenali (Mathematical Sciences Department). More details about their project can be found at http://www.amalthea-reu.org.

In this talk, you will learn the basics of dimensionality reduction. The first algorithm that is presented is the principal component analysis (PCA) which is based on explaining the variance in the data set. You will learn how to select a subset of dimensions while maintaining the most information about your data, as to, for example, make a classifier. A quick presentation of the Gaussian Process Factor Analysis follows. This algorithm extract trajectories of a system state in lower dimension space.
Speaker: Frederic Simard
Contact: [email protected]
Website: www.atomsproducts.com

PyData DC 2016
This talk provides a step-by-step overview and demonstration of several dimensionality (feature) reduction techniques. Attendees should have some basic level of understanding of data wrangling and supervised learning. The presentation will also include snippets of Python code, so familiarity with Python code will be useful.

In this R video, we'll see how PCA can reduce a 1000+ variable data set into 10 variables and barely lose accuracy! Walkthrough & code: http://amunategui.github.io/high-demensions-pca/
Note: data source url in the video no longer works, see the walkthrough for new source: http://amunategui.github.io/high-demensions-pca/
Note: for those that can't use xgboost - I added an alternative script using GBM in the walkthrough:
http://amunategui.github.io/high-demensions-pca/
Top of the page under resources look for link: "Alternative GBM Source Code - for those that can't use xgboost"
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Full lecture: http://bit.ly/PCA-alg
The number of attributes in our data is often a lot higher than the true dimensionality of the dataset. This means we have to estimate a large number of parameters, which are often not directly related to what we're trying to learn. This creates a problem, because our training data is limited.

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When there are a lot of variables, it becomes difficult to extract data. We need to devise something that will let us gather data in less number of variables. Dimensionality reduction provides you with that solution.
• Get a numerical dataset
• Calculate the covariance matrix
• Create a feature vector. Create low dimension data.
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NOTE: On April 2, 2018 I updated this video with a new video that goes, step-by-step, through PCA and how it is performed. Check it out!
https://youtu.be/FgakZw6K1QQ
RNA-seq results often contain a PCA or MDS plot. This StatQuest explains how these graphs are generated, how to interpret them, and how to determine if the plot is informative or not. I've got example code (in R) for how to do PCA and extract the most important information from it on the StatQuest website: https://statquest.org/2015/08/13/pca-clearly-explained/
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In this Data Mining Fundamentals tutorial, we discuss another way of dimensionality reduction, feature subset selection. We discuss the many techniques for feature subset selection, including the brute-force approach, embedded approach, and filter approach. Feature subset selection will reduce redundant and irrelevant features in your data.
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